GEO vs SEO: what's the difference, and do you need both?
SEO works on how your pages rank in search results. GEO works on whether AI answers name your company when a buyer asks about your category, and whether they describe it correctly. You need both, but rarely as two separate programmes: SEO is the shared foundation, and GEO adds a different unit of measurement (buyer prompts instead of keyword positions) and more attention to what other sites say about you.
GEO stands for generative engine optimization: the work of making a company findable, and accurately described, in answers from ChatGPT, Perplexity, Gemini and Google's AI features. For the full definition, read what GEO is and how it works. You will also see AEO, answer engine optimization, used for closely related work. This page sticks to GEO and SEO.
The difference between GEO and SEO at a glance
Table: GEO and SEO compared on six practical dimensions.
| SEO | GEO | |
|---|---|---|
| Goal | Rank your pages in search results and earn the click | Be named, and described accurately, in AI answers to buyer questions |
| Where the result appears | Search results pages | Answers in ChatGPT, Perplexity, Gemini and Google's AI features, such as AI Overviews |
| Unit of measurement | Positions for target keywords | A fixed set of buyer prompts: the questions a buyer asks when comparing suppliers |
| Role of third-party sources | Supporting: links and mentions help your own pages rank | Central: answers often cite other sites, and those sites shape how you are described |
| How you measure it | Rankings, impressions, clicks and traffic, with long-established tools and conventions | Whether you appear, where, how you are described and which sources are cited, per engine, re-run on the same prompts; no standard method yet |
| What the company controls | Its own site: content, technical setup, structured data | Its own site, its product and business data, its claims and proof; third-party sources only through influence; never which sources an engine picks |
Google's AI features sit in both columns. Google treats optimizing for them as SEO (more on that below), but they are also answers your buyers read, so a GEO measurement includes them alongside the other engines.
Is GEO the same as SEO?
Partly. In Google, the overlap is close to complete:
Google's generative AI features rely on its core Search ranking and quality systems; from Google's perspective, optimizing for them is still SEO. (Google Search Central documentation, updated 10 July 2026)
So if your pages can't be crawled, indexed or ranked, Google's AI features have little of yours to work with. Technical readiness and content that answers buyer questions are SEO work, and in Google they are also the base for AI visibility.
The same logic applies to product and business data:
Google says Merchant Center feeds and Business Profiles can help products and services appear in AI responses. (Google Search Central documentation, updated 10 July 2026)
For ecommerce and local businesses, keeping feeds and Business Profiles complete and accurate is not a separate GEO project. It is search work that serves both.
Outside Google, the picture is less documented and the measurement is different. That is where GEO earns its own name.
How marketers see it
Most marketers don't treat AI search as a separate discipline:
77% of marketers place AI search inside SEO (55% an extension, 22% partly overlapping); 15% see it as separate. (Semrush; 481 marketers, June 2026)
Where they struggle is measurement:
49% of marketers find it hard to measure AI's impact on pipeline or revenue; 40% still check AI answers by hand. (Semrush; 481 marketers, June 2026)
Read together, the label matters less than the measurement gap. A team that already runs SEO has much of the base for AI visibility. What many teams lack is a repeatable way to see what AI answers say about them, and whether that changes after the work.
Where the difference is real
When buyers turn to AI
Buyers use LLMs most in the middle of the journey, to compare vendors, not to discover them. (6sense, 2025 B2B Buyer Experience Report; ~4,000 B2B buyers, purchases from $25k)
This shapes what you track. A keyword list tells you how you rank for terms. A buyer prompt set tells you what an AI answer says when a buyer asks it to compare: which suppliers fit a use case, how two vendors differ, what the alternatives to a known vendor are. Being left out of those answers, or described wrongly in them, can cost you a place in the comparison.
Where AI answers get their sources
In our own study of companies drawn from the public ESA BIC Noordwijk incubatee and alumni listings, we logged the sources that AI answers cited:
Only 1.2% of 1,798 logged citations pointed to the companies' own websites. (Our own study; tests on 25 and 26 August 2026)
In that sample, almost everything the answers cited lived on other sites. SEO measures your own pages. GEO also has to account for the third-party record: what independent sources say about you, and whether it is accurate and current. That is the clearest practical difference between the two.
It is one sample, tested on two days. It shows a pattern in that group, not a rule for every market. Read the full study.
Where llms.txt fits
Google Search ignores llms.txt: it neither helps nor harms visibility there. (Google Search Central documentation, updated 10 July 2026)
llms.txt is a proposed file that summarises a site for language models. For engines other than Google, we haven't seen evidence either way, so it doesn't sit high in our plans. If you are deciding where the first weeks go, the steps below come first.
What to do first
A practical order. None of it guarantees that an AI engine will name you, because engines choose their own sources. Each step makes the next one measurable.
- Check the search basics. Make sure your key pages can be crawled, indexed and found for the terms that matter. If you sell products or serve a local area, check that your Merchant Center feeds and Business Profile are complete and accurate.
- Write down your buyers' questions. Build a fixed set of prompts a buyer would ask when comparing suppliers in your category. Keep the wording stable so results stay comparable over time.
- Take a baseline before changing anything. Run the set in the engines your buyers use. Record whether you appear, where, how you are described and which sources the answers cite.
- Fix what you control. Make what you do, for whom and with what proof easy to find and consistent across your own site and profiles. Correct inaccurate descriptions at the source where you can.
- Work on the third-party record. Look at the sources the answers cite in your category, and at where your company is missing from them or described wrongly.
- Re-measure on a fixed cadence. Re-run the same prompt set, for example monthly. Look at trends across the whole set rather than single answers, since answers can change from one run to the next.
For a quick first read on step 3, the Free AI Visibility Check runs 10 buyer-style prompts for your category and returns results within 48 hours. Foundation records a full baseline on 30-50 prompts across ChatGPT, Perplexity, Gemini and Google AI Overviews and turns it into a prioritised roadmap. More on the ongoing work: how CitedLift approaches AI visibility.
What we don't know yet
- Surveys point to different stages of the journey. 51% of B2B software buyers start research with an AI chatbot more often than Google. (G2, The Answer Economy; 1,076 B2B decision-makers, March 2026, global) The 6sense finding above puts the peak of AI use in the middle of the journey instead. The samples differ, and so do the questions asked. Treat each figure as one view, not a settled fact.
- There is no standard way to measure GEO. Prompt sets, engines and scoring differ between teams and tools, so figures from different sources rarely compare. SEO reporting has long-established conventions; AI visibility measurement does not have them yet.
- Outside Google, we work from observation. Google documents how its AI features relate to Search. For other engines, our understanding comes mostly from observing their answers, not from documentation.
- Answers change. One measurement is a snapshot. Patterns need repeated runs on the same prompts.
- Our own data is one sample. The citation figure above comes from one group of companies tested on two days. It describes that group; it is not a benchmark for your market.
- No one controls the outcome. AI engines choose their own sources. The work can improve what is available and accurate about you; it cannot guarantee inclusion.
Sources
Every figure on this page sits next to its source. More figures on AI search and buyer behaviour, with sources: AI search statistics.